机器学习辅助的半导体带隙预测具有低度兴奋剂的半导体
Yuqi Tang1, Haiyuan Chen1, Jianwei Wang1
1School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China. jianwei_wang@uestc.edu.cn.
Physical chemistry chemical physics : PCCP
|June 29, 2023
概括
机器学习 (ML) 模型准确预测半导体带间隙,即使在极低的兴奋剂度下也是如此. 这种方法将密度函数理论 (DFT) 与ML相结合,用于高效的材料性质预测.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 预测材料特性,如带间隙,对于设备设计至关重要.
- 传统方法包括实验和密度函数理论 (DFT) 计算.
- 机器学习 (ML) 为属性预测提供了一个有效的替代方案.
研究的目的:
- 开发一种方法来预测具有极低兴奋剂度的半导体的带间隙.
- 解决目前处理稀释兴奋剂案件的方法的局限性.
- 为了加速对新型半导体材料的物理性质的预测.
主要方法:
- 结合密度函数理论 (DFT) 计算与机器学习 (ML) 预测模型.
- 使用对称标准的配置选构建的半导体结构.
- 将3D结构变化映射到1D特征以用于ML模型输入;采用了几次拍摄的学习.
主要成果:
- 成功预测了半导体的带间隙与正常的兴奋剂度.
- 对于极低度的合半导体,实现了高精度,与与化化 GaAs 的 DFT 相比,误差低于10%.
- 使用培训外和测试数据集验证了ML模型性能,证明了稳定性.
结论:
- 开发的ML方法有效地预测了半导体中的频段差距,特别是在挑战极低兴奋剂场景时.
- 这种方法显著加速了半导体物理性质的预测,有助于设计新设备.
- DFT和ML的整合为材料发现和表征提供了一个强大的工具.
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